Cultivating students' computational thinking (CT) has become an educational priority, with the implementation of effective instructional programs identified as a key means of fostering these skills. However, traditional mathematics teaching in many contexts often relies on lecture-based approaches that emphasize procedural skills over problem-solving, which may limit students' engagement and CT development. This study designed a CT-integrated mathematics instruction framework for secondary mathematics, supported by GGB programming, and conducted a case study to evaluate its efficacy. Using an experimental research approach, 74 middle school students from a public school in Hangzhou, China were divided into an experimental group and a control group. The experimental group received CT-integrated mathematics instruction, while the control group followed conventional teaching methods. Data were collected using the Computational Thinking Scale (CTS) and a Mathematics Learning Interest Inventory for quantitative analysis, supplemented by qualitative insights from student interviews. The results indicate that the experimental group demonstrated significantly higher levels of CT and greater interest in mathematics compared to the control group, with the most notable improvement observed in algorithmic thinking.
Xu et al. (Fri,) studied this question.